In medical imaging, techniques and processes are used to create images of one or more body parts of a living being i.e. anatomical regions, e.g. those of a human body, for clinical purposes and/or medical science. In particular, the internal anatomy of a patient can be imaged to determine how a certain disease has progressed, so that surgical persons are able to distinguish between infected tissues and healthy tissues within the patient. In radiation therapy, such images can be utilized to determine the radiation dose applied to the patient, so that the therapy can be planned such that the amount of radiation the patient receives is minimized while still achieving the goals of therapy.
In general, CT images are used for RT dosimetry. CT images comprising voxel grey values are usually measured in Hounsfield Units (HU), which can be directly translated into electron densities or attenuation coefficients. Hence, the measured HU values can be directly calculated into radiation dosage. However, increasingly often MR images are acquired for diagnostic purposes or organ delineation prior to RT treatment planning. Dose calculation based only on MR images is viewed as being highly beneficial, since this would eliminate the need for generating additional CT images for dose calculations and thus simplifies the workflow and reduce the radiation amount applied to a patient.
To date, there are approaches known from the literature which are used to create estimated density maps or attenuation maps from MR images for RT planning. Due to the physics of the image acquisition, MR intensities do not uniquely correspond to electron densities or attenuation coefficients. Hence, the afore-mentioned maps cannot be derived from the MR images by a simple look-up operation, as is commonly done when deriving these maps from CT images. Solutions proposed so far suffer from a series of shortcomings. For instance, registration of a CT-based density atlas to the MR image may help in regions, where the atlas values are confined and reliable, e.g. the brain. However, in highly variable anatomical regions such as the pelvic region, registration may not be able to cover the anatomical variations between patients, e.g. bladder/bowel filling or movement, resection of structures (e.g. kidneys, liver parts) or pathologic changes.
U.S. Pat. No. 8,588,498 B2 discloses a method for segmenting bones on MR images, including retrieving an MR image and performing an enhancement process on the MR image to generate a bone enhanced MR image. The bone enhanced MR image is then registered to a CT-based bone atlas. An MR image with bone segmentation is generated by segmenting the bone enhanced MR image using the CT-based bone atlas as a mask.
Peter B. Greer et al., “A magnetic resonance imaging-based workflow for planning radiation therapy for prostate cancer”, Medical Journal of Australia, 1 Jan. 2011, discloses a method for creating pseudo-CT scan from MRI scan, wherein the method comprises retrieving an MRI scan of a patient, defining prostate and organ contours, registering a CT electron densities atlas to a plurality of tissues by mapping electron densities to the tissues, resulting in a pseudo-CT scan with electron densities mapped to the patient's MRI scan.
JASON A. DOWNLING ET AL., “An Atlas-Based Electron Density Mapping Method for Magnetic Resonance Imaging (MRI)-Alone Treatment Planning and Adaptive MRI-Based Prostate Radiation Therapy”, International Journal of Radiation Oncology, 1 May 2012, discloses an automatic method to generate realistic electron density information (pseudo-CT) from MRI scans for prostate radiation therapy.
M. HOFMANN ET AL., “MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods”, THE JOURNAL OF NUCLEAR MEDICINE, vol. 52, no. 9, 1 Sep. 2011, discloses algorithms for whole-body MRI-based AC (MRAC), including a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR).